The Study on Predicting Respiratory Motion with Support Vector Regression

被引：0
|
作者：
Tong, Lei [1 ]
Chen, Chaomin [2 ]
Kang, Kailian [2 ]
Xu, Zihai [3 ]
机构：
[1] Guangdong Vocat Coll Mech & Elect Technol, Guangzhou 510515, Guangdong, Peoples R China
[2] Southern Med Univ, Inst Biomed Engn, Guangzhou 510515, Guangdong, Peoples R China
[3] PLA 303 Hosp, Radiotherapy Ctr, Nanning 530021, Peoples R China
来源：
PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON COMPUTER MODELING, SIMULATION AND ALGORITHM (CMSA 2018) | 2018年 / 151卷
关键词：
radiotherapy; support vector regression; respiratory motion prediction; kernel function;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Objective: The target is usually tracked in real time at thoracic and abdominal radiotherapy due to the effect of respiratory motion, the prediction is necessary to compensate the system latency. Method: This paper proposed a prediction method based on support vector regression, it dynamically updates the training set and achieves the accurate online support vector regression. Result: The experiment selected seven respiratory motion data, using online model trained and predicted. The mean absolute error was 0.30mm. Conclusion: The online accurate support vector regression described respiratory motion accurately, and the results with high precision can be satisfied in practical application.
引用
收藏
页码：204 / 207
页数：4
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